Machine Learning Architect

Ayosemi; Boston (MA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Equity

Health insurance

Dental insurance

Vision insurance

Job summary

Ayosemi, a venture-backed startup in Boston, is seeking a founding team member with deep expertise in machine learning to develop specialized processors. This role involves deploying models on prototype hardware and collaborating with hardware engineers to shape processor architecture.

Ideal candidates will have a PhD in machine learning and experience in distributed training. This position offers a competitive salary, equity, and the opportunity to impact AI technology significantly. All work is onsite in Boston, fostering a collaborative environment.

Qualifications

• Experience deploying and running models on prototype hardware.
• Ability to adapt algorithms for novel processing environments.
• Deep understanding of neural networks and their operations.

Responsibilities

• Deploy and run models on prototype hardware.
• Develop algorithms for training on new hardware.
• Work with hardware engineers to refine processor architecture.

Skills

Machine learning

Distributed training

Python

PyTorch

TensorFlow

JAX

Education

PhD in machine learning or related field

Job description

Come build the future of compute with us.

Location

Employment Type

Full time

Location Type

On-site

Department

About Ayo

We are a venture-backed early-stage startup developing processors specialized for machine learning. The processor will provide orders or magnitude improvement in speed and power efficiency with a goal of unseating the GPU as the dominant computing platform for AI.

The Role

We are seeking a deep ML practitioner to join as a founding team member - someone with hands‑on experience working on or alongside a foundation model at scale, who understands what happens under the hood when splitting jobs across thousands of GPUs, and who is excited to bring that depth to novel hardware. They have a full‑stack understanding of machine learning architectures, love to optimize algorithms across disciplinary boundaries, and will deploy and train models directly on our prototype chips to help us prove out what our processor can do - no prior hardware experience required.

What You’ll Do:

• Deploy and run trained models on prototype hardware and digital twins, producing working demonstrations on our chips.
• Develop and adapt algorithms to train models on novel processing environments, including our prototype hardware.
• Work with hardware engineers to define and refine processor architecture based on insights learned through model training and experimentation.
• Maintain a deep curiosity about what makes machine learning systems work - and bring that curiosity to bear on how they run on new hardware.

What We’re Looking For:

• PhD in machine learning, representation learning, theory of computation, or a related field - or equivalent industry experience working on foundation models at scale.
• Experience training models at scale - distributed training across many GPUs, working with large datasets and compute.
• Has built and trained neural networks from scratch.
• Deep knowledge of the structure and internal operation of neural networks - including how and why they behave the way they do (e.g., interpretability or explainability work is a plus).
• Excitement about applying deep AI expertise to new and novel hardware environments - you don’t need prior experience with photonics or silicon, but you want to learn.
• Fluent knowledge of Python.
• Fluency in PyTorch (preferred), TensorFlow, JAX, or other industry‑standard ML software libraries.

Why Ayo

You'll be a founding AI team member - shaping how we think about and deploy AI as a company.

We are working on a genuinely hard and interesting problem: unseating the GPU as the dominant AI compute platform.

Early‑stage means real ownership, real impact, and meaningful equity.

Competitive salary. Equity commensurate with stage and seniority. Benefits package including health, dental, and vision.

Fully onsite in Boston - we are a collaborative, in‑person team.